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arxiveess.SY2026-07-22

Human-Aware Power Restoration for Fair Outage Experience in Distribution Systems

Majid Dehghani, Arastoo H Salimi, Hamidreza Nazaripouya

This paper proposes a novel methodology for human-aware and fair service restoration in power distribution networks, explicitly accounting for the customer experience of outage duration. The complexity of this problem stems from the inherently unpredictable and stochastic nature of power outage events. Traditional approaches often oversimplify the problem by treating failures as deterministic, overlooking the lived experiences of customers and the true uncertainty of outage patterns. In contrast, the proposed method incorporates the probability of potential failures to guide a customer-aware and fairness-driven resource allocation, ensuring that restoration is not only fast but also perceived as fair from the customer's perspective. To achieve this, a spatially distributed, adaptive, and scalable partitioning policy is designed to balance restoration time across all failure locations, promoting consistency and equity in the outage experience. Next, an adaptive and distributed repair crew dispatch algorithm is proposed to accelerate service restoration while ensuring that no customer segment is disproportionately affected. The framework leverages a Receding Horizon (RH) optimization algorithm to dynamically minimize total restoration time amid randomly occurring outages in both space and time. Simulation results on modified 69-bus distribution networks under stochastic outage conditions demonstrate the model's effectiveness in delivering socially fair and customer-sensitive restoration outcomes

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arxiveess.SY2026-07-24

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arxivcs.ROeess.SY2026-07-24

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arxivcs.NIcs.MAeess.SY2026-07-24

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arxiveess.SY2026-07-24

Physics-Informed Neural Network for Modeling the Dynamic Behavior of Grid-Forming Converters

Hussein Jaffal, Arianna Fois, Sarra Bouchkati, Amirali Mahjoob, Andreas Ulbig

This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and…

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arxiveess.SY2026-07-24

StateFormer: A Multivariate Transformer for Learning History-Dependent Battery State Dynamics and Long-Horizon Health Forecasting

Zhe Bai, Stephen Harris

This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of…

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